Thesis Defense: Dr. Jong Wha (Joanne) Joo

Jong Wha (Joanne) Joo successfully defended her thesis,”Design of efficient and accurate statistical approaches to correct for confounding effects in genetic association studies,” on Friday, December 4, 2015 in Boelter 4760.  Her talk, which is posted on our YouTube channel ZarlabUCLA, discusses using a mixed model analysis (GAMMA) to efficiently analyzes large numbers of phenotypes while simultaneously considering population structure, an expression quantitative trait loci (eQTL) mapping tool to eliminate spurious hotspots while retaining genuine regulatory hotspots, and a multiple testing correction method (slideLMM) for linear mixed models.
More details about her research are available in the three papers she discusses:

Joo, Jong Wha J; Hormozdiari, Farhad; Han, Buhm; Eskin, Eleazar

Multiple testing correction in linear mixed models. Journal Article

In: Genome Biol, 17 (1), pp. 62, 2016, ISSN: 1474-760X.

Abstract | Links | BibTeX

Joo, Jong Wha J; Kang, Eun Yong; Org, Elin; Furlotte, Nick; Parks, Brian; Lusis, Aldons J; Eskin, Eleazar

Efficient and Accurate Multiple-Phenotypes Regression Method for High Dimensional Data Considering Population Structure Book Chapter

In: Research in Computational Molecular Biology, pp. 136-153, Springer International Publishing, 2015.

Abstract | Links | BibTeX

Joo, Jong Wha J; Sul, Jae Hoon ; Han, Buhm ; Ye, Chun ; Eskin, Eleazar

Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies. Journal Article

In: Genome Biol, 15 (4), pp. R61, 2014, ISSN: 1465-6914.

Abstract | Links | BibTeX

Thesis Defense: Dr. Zhanyong (Jerry) Wang

Jerry Wang defended his thesis on September 8, 2014 in 4760 Boelter Hall.

His thesis topic was Efficient Statistical Models For Detection And Analysis Of Human Genetic Variations. The video of his full defense can be viewed on the ZarlabUCLA YouTube page here.


In recent years, the advent of genotyping and sequencing technologies has enabled human genetics to discover numerous genetic variants. Genetic variations between individuals can range from Single Nucleotide Polymorphisms (SNPs) to differences in large segments of DNA, which are referred to as Structural Variations (SVs), including insertions, deletions, and copy number variations (CNVs).

First proposed was a probabilistic model, CNVeM, to detect CNVs from High-Throughput Sequencing (HTS) data. The experiment showed that CNVeM can estimate the copy numbers and boundaries of copied regions more precisely than previous methods.

Genome-wide association studies (GWAS) have discovered numerous individual SNPs involved in genetic traits. However, it is likely that complex traits are influenced by interaction of multiple SNPs. In his thesis, Jerry proposed a two-stage statistical model, TEPAA, to reduce the computational time greatly while maintaining almost identical power to the brute force approach which considers all combinations of SNP interactions. The experiment on the Northern Finland Birth Cohort data showed that TEPAA achieved 63 times speedup.

Another drawback of GWAS is that rare causal variants will not be identified. Rare causal variants are likely to be introduced in a population recently and are likely to be in shared Identity-By-Descent (IBD) segments. Jerry proposed a new test statistic to detect IBD segments associated with quantitative traits and made a connection between the proposed statistic and linear models so that it does not require permutations to assess the significance of an association. In addition, the method can control population structure by utilizing linear mixed models.


The full paper on topics covered in Jerry’s thesis defense can be found below:

Wang, Zhanyong; Sul, Jae Hoon ; Snir, Sagi ; Lozano, Jose A; Eskin, Eleazar

Gene-Gene Interactions Detection Using a Two-Stage Model Book Chapter

In: Research in Computational Molecular Biology, pp. 340-355, Springer International Publishing, 2014.

Abstract | Links | BibTeX

Video Tutorial: An Introduction to Read Mapping and Next Generation Sequencing

We teach a course called “Computational Genetics” each year at UCLA. This course is taken by both graduate and undergraduate students from both the Computer Science department and the many biology and medical school programs. In this course we cover both topics related to genome wide association studies (GWAS) and topics related to next generation sequencing studies. One lecture that is given each year is an introductory lecture to sequencing and read mapping. The video of this lecture is available here. Please excuse the poor cinematography. This lecture was recorded from the back of the classroom.